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AWS Lambda is a serverless computing service that lets you run code without provisioning or managing servers. It's ideal for applications that need to scale automatically and process events in real-time.

Prerequisites

An AWS account
Basic understanding of AWS services

Step-by-Step Guide

1. Create a Lambda Function

Launch Lambda: In the AWS Management Console, search for "Lambda" and launch the service.
Create Function: Click on "Create function".
Provide Function Details: Enter a name for your function, select a runtime (e.g., Python, Node.js), and choose a trigger (e.g., S3, API Gateway).

Image of AWS Lambda Create Function screen

2. Write Function Code

Write Code: Write your function code using the selected runtime.

Python
import json

def lambda_handler(event, context):
    return {
        'statusCode': 200,
        'body': json.dumps('Hello from Lambda!')
    }

3. Configure Triggers (Optional)

Configure Triggers: If you selected a trigger, configure it to invoke your function. For example, if you chose S3, configure an S3 bucket and event.

4. Deploy Function

Deploy Function: Deploy your function to AWS.

5. Test Function

Test Function: Test your function using the test event provided by Lambda or by invoking it from your trigger.

Image of AWS Lambda Test Function screen

Additional Considerations

Runtimes: Choose from a variety of runtimes supported by Lambda.
Triggers: Lambda supports various triggers, including API Gateway, S3, Kinesis, and more.
Concurrency: Configure concurrency limits to control the number of instances of your function that can run simultaneously.
Environment Variables: Set environment variables to pass configuration information to your function.

AWS EC2 Image Builder LAB

AWS EC2 Image Builder is a fully managed service that makes it easy to create custom Amazon Machine Images (AMIs) for use with EC2 instances. It automates the process of building and configuring AMIs, saving you time and effort.

Prerequisites

An AWS account
Basic understanding of AWS services

Step-by-Step Guide

1. Create an Image Pipeline

Launch Image Builder: In the AWS Management Console, search for "Image Builder" and launch the service.
Create Pipeline: Click on "Create image pipeline".
Provide Pipeline Details: Enter a name for your pipeline and select the desired configuration settings.

Image of AWS EC2 Image Builder Create Pipeline screen

2. Configure Components

Configure Components: Configure the components that make up your pipeline, including the base image, infrastructure configuration, and build specifications.

Image of AWS EC2 Image Builder Configure Components screen

3. Create Image

Create Image: Create an image based on your pipeline configuration.

Image of AWS EC2 Image Builder Create Image screen

4. View Image Details

View Details: View the details of your created image, including the image ID and creation time.

Image of AWS EC2 Image Builder View Image Details screen

5. Launch Instance

Launch Instance: Launch an EC2 instance using the created image.

Additional Considerations

Customizations: Customize your image pipeline to meet your specific requirements.
Automation: Integrate Image Builder with other AWS services like CodePipeline for automated image creation.
Version Control: Use version control to manage changes to your image pipeline.
Sharing: Share images with other AWS accounts.

AWS Batch LAB

AWS Batch is a fully managed compute service that makes it easy to run batch jobs at any scale. It eliminates the need to provision and manage EC2 instances, so you can focus on your workloads.

Prerequisites

An AWS account
Basic understanding of AWS services

Step-by-Step Guide

1. Create a Compute Environment

Launch Batch: In the AWS Management Console, search for "Batch" and launch the service.
Create Compute Environment: Click on "Create compute environment".
Provide Details: Enter a name for your compute environment and select the desired configuration settings (e.g., instance type, compute environment type).

Image of AWS Batch Create Compute Environment screen

2. Create a Job Queue

Create Job Queue: Click on "Create job queue".
Provide Details: Enter a name for your job queue and associate it with your compute environment.

Image of AWS Batch Create Job Queue screen

3. Create a Job Definition

Create Job Definition: Click on "Create job definition".
Provide Details: Specify the container image, command, and other parameters for your job.

Image of AWS Batch Create Job Definition screen

4. Submit a Job

Submit Job: Click on "Submit job".
Provide Details: Enter a name for your job, select the job queue, and specify the job definition.

5. Monitor Job

Monitor Job: Track the status of your job and view the output.

Additional Considerations

Scheduling: Schedule jobs to run at specific times or intervals.
Dependency Management: Manage dependencies between jobs using job definitions.
Custom Compute Environments: Create custom compute environments to meet your specific requirements.
Integration: Integrate Batch with other AWS services like S3 and CloudWatch.

AWS App Runner LAB

AWS App Runner is a fully managed service that makes it easy to deploy and scale web applications. It takes care of all the infrastructure, so you can focus on building your applications.

Prerequisites

An AWS account
A source code repository (e.g., GitHub, GitLab)
A Dockerfile or build specification

Step-by-Step Guide

1. Create a Service

Launch App Runner: In the AWS Management Console, search for "App Runner" and launch the service.
Create Service: Click on "Create service".
Provide Service Details: Enter a name for your service and select your source code repository.

Image of AWS App Runner Create Service screen

2. Configure Service

Configure Service: Specify the build and deployment settings for your service, including the Dockerfile or build specification, environment variables, and scaling configurations.

Image of AWS App Runner Configure Service screen

3. Create Service

Create Service: Click on "Create service" to deploy your application.

4. View Service Details

View Details: Once your service is deployed, you can view its details, including the URL, status, and metrics.

Image of AWS App Runner View Service Details screen

5. Access Application

Access Application: Use the provided URL to access your deployed application.

Additional Considerations

Deployment Strategies: Choose from different deployment strategies like Canary deployments or Blue/Green deployments.
Scaling: App Runner automatically scales your application based on traffic.
Custom Domains: Use custom domains to map your application to a specific domain name.
Integration: Integrate App Runner with other AWS services like CodePipeline for continuous delivery.

AWS Fraud Detector LAB

AWS Fraud Detector is a managed service that helps you build, train, and deploy machine learning models to detect fraud in your applications. It provides pre-built machine learning models and tools to help you identify fraudulent activity.

Prerequisites

An AWS account
Basic understanding of AWS services

Step-by-Step Guide

1. Create a Detector

Launch Fraud Detector: In the AWS Management Console, search for "Fraud Detector" and launch the service.
Create Detector: Click on "Create detector".
Provide Detector Details: Enter a name for your detector and select the desired configuration settings (e.g., region, data privacy).

Image of AWS Fraud Detector Create Detector screen

2. Create Labels

Create Labels: Create labels to represent fraudulent and legitimate events in your data.

Image of AWS Fraud Detector Create Labels screen

3. Ingest Data

Ingest Data: Ingest your historical data into Fraud Detector. You can either upload a file or use a data source like Kinesis Data Streams.

Image of AWS Fraud Detector Ingest Data screen

4. Train Model

Train Model: Train a machine learning model using your labeled data. Fraud Detector provides pre-built models or you can create your own.

Image of AWS Fraud Detector Train Model screen

5. Create a Detector Version

Create Version: Create a version of your detector to deploy.

6. Deploy Detector

Deploy Detector: Deploy your detector to an endpoint.

Image of AWS Fraud Detector Deploy Detector screen

7. Use Detector

Use Detector: Send real-time events to the detector endpoint to receive fraud predictions.

Python
import boto3

# Create a Fraud Detector client
client = boto3.client('frauddetector')

# Send an event to the detector
response = client.detect_events(
    DetectorId='your-detector-id',
    Events=[
        {
            'EventId': 'event-1',
            'EventType': 'transaction',
            # Other event data
        }
    ]
)

Additional Considerations

Pre-built Models: Use pre-built models for common fraud scenarios.
Custom Models: Create custom models for more tailored fraud detection.
Integration: Integrate Fraud Detector with other AWS services like Lambda and Kinesis.
Monitoring: Monitor the performance of your detector and make adjustments as needed.

AWS DevOps Guru LAB

AWS DevOps Guru is a service that proactively identifies anomalies in your applications. It uses machine learning to analyze your application metrics and logs to detect potential issues.

Prerequisites

An AWS account
Running applications on AWS

Step-by-Step Guide

1. Enable DevOps Guru

Launch DevOps Guru: In the AWS Management Console, search for "DevOps Guru" and launch the service.
Enable DevOps Guru: Enable DevOps Guru for your account.

2. Configure Resources

Configure Resources: Specify the AWS resources you want DevOps Guru to analyze.

Image of AWS DevOps Guru Configure Resources screen

3. View Anomalies

View Anomalies: DevOps Guru will analyze your resources and identify any anomalies. You can view these anomalies in the console.

Image of AWS DevOps Guru View Anomalies screen

4. Investigate Anomalies

Investigate Anomalies: Click on an anomaly to view more details, including the root cause and potential solutions.

Image of AWS DevOps Guru Investigate Anomalies screen

5. Take Action

Take Action: Based on the insights provided by DevOps Guru, take the necessary steps to address the identified anomalies.

Additional Considerations

Custom Metrics: Provide custom metrics to DevOps Guru for more accurate anomaly detection.
Integration: Integrate DevOps Guru with other AWS services like CloudWatch and X-Ray for comprehensive monitoring and analysis.
Automation: Automate response actions using AWS Lambda and other services.

AWS DeepComposer LAB

AWS DeepComposer is a hands-on machine learning tool designed for developers and data scientists to experiment with and learn about generative AI. It provides a physical device and a cloud-based environment for training and deploying generative models.

Prerequisites

An AWS account
AWS DeepComposer device

Step-by-Step Guide

1. Set Up DeepComposer

Connect to Wi-Fi: Connect your DeepComposer device to a Wi-Fi network.
Sign In: Sign in to your AWS account using the DeepComposer device.

2. Launch DeepComposer Studio

Launch Studio: Launch the DeepComposer Studio application on the device.

Image of AWS DeepComposer Studio launch screen

www.cloudbrigade.com

3. Create a Project

Create Project: Create a new project in DeepComposer Studio.
Select Dataset: Choose a dataset from the available options or upload your own.

Image of AWS DeepComposer Create Project screen

4. Train Model

Train Model: Train a generative model using the selected dataset. You can experiment with different hyperparameters to optimize the model's performance.

Image of AWS DeepComposer Train Model screen

aws.amazon.com

5. Generate Samples

Generate Samples: Use the trained model to generate new samples.

Image of AWS DeepComposer Generate Samples screen

6. Experiment and Explore

Experiment: Explore different generative models and techniques.
Create Custom Models: Create your own custom generative models.

Additional Considerations

Datasets: Experiment with different datasets to see how they impact the generated samples.
Hyperparameters: Tune hyperparameters to optimize the performance of your models.
Visualization: Use the DeepComposer Studio to visualize the generated samples and understand the model's behavior.

AWS Comprehend Medical LAB

AWS Comprehend Medical is a natural language processing (NLP) service specifically designed for healthcare and life sciences applications. It helps you extract insights from medical text, such as clinical notes, medical reports, and patient records.

Prerequisites

An AWS account
Basic understanding of AWS services

Step-by-Step Guide

1. Create a Comprehend Medical Job

Launch Comprehend Medical: In the AWS Management Console, search for "Comprehend Medical" and launch the service.
Create Job: Click on "Create job".
Provide Job Details: Enter a name for your job, select the desired feature (e.g., entity recognition, medical terminology extraction), and specify the input data.

Image of AWS Comprehend Medical Create Job screen

2. Submit Data

Submit Data: Submit the medical text data you want to analyze. You can either upload a file or provide the text directly.

3. Monitor Job

Monitor Job: Track the progress of your job and view the results.

Image of AWS Comprehend Medical Monitor Job screen

4. Analyze Results

Analyze Results: Review the results of your job, which will vary depending on the feature you selected. For example, entity recognition will identify medical entities like medications, diagnoses, and procedures.

Additional Considerations

Features: Comprehend Medical offers a variety of features, including entity recognition, medical terminology extraction, and relationship extraction.
Data Format: Ensure your data is in a supported format (e.g., text file, JSON).
Data Privacy: Be mindful of data privacy regulations when handling medical data.
Custom Models: Create custom models for more tailored results.

AWS Comprehend LAB

AWS Comprehend is a natural language processing (NLP) service that helps you understand text and extract insights from it. It offers features like sentiment analysis, entity recognition, and topic modeling.

Prerequisites

An AWS account
Basic understanding of AWS services

Step-by-Step Guide

1. Create a Comprehend Job

Launch Comprehend: In the AWS Management Console, search for "Comprehend" and launch the service.
Create Job: Click on "Create job".
Provide Job Details: Enter a name for your job, select the desired feature (e.g., sentiment analysis, entity recognition), and specify the input data.

Image of AWS Comprehend Create Job screen

2. Submit Data

Submit Data: Submit the text data you want to analyze. You can either upload a file or provide the text directly.

3. Monitor Job

Monitor Job: Track the progress of your job and view the results.

Image of AWS Comprehend Monitor Job screen

4. Analyze Results

Analyze Results: Review the results of your job, which will vary depending on the feature you selected. For example, sentiment analysis will provide a sentiment score and label, while entity recognition will identify entities in the text.

Additional Considerations

Features: Comprehend offers a variety of features, including sentiment analysis, entity recognition, topic modeling, key phrase extraction, and custom labels.
Data Format: Ensure your data is in a supported format (e.g., text file, JSON).
Batch Processing: For large datasets, use batch processing to analyze the data more efficiently.
Custom Models: Create custom models for more tailored results.

AWS CodeGuru LAB

AWS CodeGuru is a service that helps you identify performance and code quality issues in your Python and Java applications. It uses machine learning to analyze your code and provide actionable recommendations.

Prerequisites

An AWS account
Basic understanding of AWS services
Python or Java application code

Step-by-Step Guide

1. Create a CodeGuru Profiler Group

Launch CodeGuru Profiler: In the AWS Management Console, search for "CodeGuru Profiler" and launch the service.
Create Group: Click on "Create group".
Provide Group Details: Enter a name for your group and select the desired configuration settings (e.g., region, retention period).

Image of AWS CodeGuru Profiler Create Group screen

2. Configure Profiling

Configure Profiling: Configure your application to enable profiling. This typically involves adding a profiling agent to your application.

3. Start Profiling

Start Profiling: Start profiling your application to collect performance data.

4. View Profiler Results

View Results: Use the CodeGuru Profiler console to view the profiling results, including flame graphs, performance metrics, and recommendations.

Image of AWS CodeGuru Profiler View Results screen

5. Create a CodeGuru Reviewer Repository

Create Repository: Create a CodeGuru Reviewer repository in your AWS account.

Image of AWS CodeGuru Reviewer Create Repository screen

6. Configure Repository

Configure Repository: Configure your repository to enable CodeGuru Reviewer to analyze your code.

7. Analyze Code

Analyze Code: CodeGuru Reviewer will analyze your code and provide recommendations for improving performance and code quality.

Image of AWS CodeGuru Reviewer Analyze Code screen

Additional Considerations

Custom Metrics: Create custom metrics to track specific performance indicators.
Integration: Integrate CodeGuru with other AWS services like CodeBuild and CodePipeline for continuous integration and delivery.
Customizations: Customize CodeGuru to meet your specific needs using configuration options and custom rules.